HiGS-Calib: A Hierarchical 3D Gaussian Splatting based Targetless Local-Consistent LiDAR-Camera Calibration Method

3D Gaussian Splatting (3DGS) has emerged as a powerful scene representation, offering geometrically dense and photometrically accurate modeling capabilities that present a promising new paradigm for accurate targetless sensor calibration. Current 3DGS-based LiDAR-camera calibration methods usually highly rely on the joint optimization of the Gaussian model and extrinsics, and thus suffer two critical limitations. On the one hand, the global optimization is usually sensitive to the accumulated localization error of LiDAR. On the other hand, the inaccurate extrinsics may cause oscillations during the joint optimization. Specifically, there is a fundamental dilemma: accurate extrinsic calibration requires accurate scene models, while constructing accurate models itself depends on accurate extrinsics. This dilemma frequently triggers oscillatory optimization trajectories, significantly increasing vulnerability to premature convergence at suboptimal states. To address these challenges, we propose HiGS-Calib, a novel 3DGS calibration pipeline featuring the integration of our proposed Local-Consistent Photometric-Geometric (LCPG) error model and the hierarchical architecture. The LCPG error leverages the spatial consistency within local windows to quantify pose misalignment using only geometric attributes of the 3DGS model, bypassing color-pose reliance constraints. Besides, diverging from joint optimization paradigms, HiGS-Calib implements coarse-to-fine iterative optimization, decoupling scene modeling from extrinsic refinement and thereby achieving stable and accurate calibration. Extensive evaluation demonstrates the significantly improved calibration accuracy and stability of our HiGS-Calib over other state-of-the-art methods. To make our results reproducible, the source code has been released at https://github.com/IRMVLab/HiGS-Calib.